Mid-Week Metrics Honors the Fallen Coach

It’s not quite the fourth quarter against Notre Dame, but Saturday had as many ups and downs on the Win Chart as any we’ve seen this year.

We’ll go with 5 plays each this week to mark the occasion.

Top Plays:

1. Play 112, 14.2%, Robinson to Odoms on 3rd and 11 to give Michigan the lead back for good while the OL gave Denard all day.

2. Play 163, 11.2%, Robinson to Dileo for 28 yards on Michigan’s final drive.

3. Play 22, 11.1%, Robinson runs for 41 yards to tie it up early.

4. Play 165, 9.5%, Robinson runs for 14 yards to keep the clock moving and the drive going late.

5. Play 137, 9.3%, the defense gets in the mix, stopping Miller on 3rd and Goal from the 2, leading to the FG instead of a touchdown.

Bottom Plays:

1. Play 7, –12.8%, Miller goes deep for the first score of the game.

2. Play 95, –12.0%, Miller goes deep a second time to give Ohio a halftime lead.

3. Play 172, –8.8%, Steve Watson’s personal foul pushed 3rd and Goal from difficult to impossible and increases the degree of difficulty on an impending field goal.

4. Play 134, –8.6%, Miller goes for 23 yards to give Ohio 1st and Goal at the 5 late in the third quarter.

5. Play 74, –7.1%, Miller uses my favorite NCAA Football play with an athletic QB, the wrong way speed option for a TD.

Ohio Game Scores

Rushing: +12, tops in Big Ten play and behind only SD St and E Michigan on the year

Passing: +11, second only to Northwestern on the season

Rush Defense: –9, worst score of the season

Pass Defense: –7, only Notre Dame was worse

Special Teams: +3, the late field goal pushed this to the top of the list for this year

Denard: As I tweeted earlier this week, Denard had the 5th best game of any QB this year at +24. It was both his best passing (+13) and best rushing (+11) game of the season. It was only the 7th +10 rushing performance by any QB this year and the first to pair it with a passing number higher then +3!

Toussaint: +1, a solid but not spectacular day.

Miller: Braxton Miller is going to be a force. His +15 (+6/+9) was his best game of the year by 6 points. His three games have been his three best. Had Ohio gone with him from the start Ohio is probably has at least 8 wins now.

Saturday’s +23 was the 9th best opponent adjusted offensive game of the year for any team and the best game in BCS conference play.

Fired Coach Dumb Punt of the Week

Several good candidates this week. Clemson punting from the 35 late in the third trailing by two touchdowns. Ohio punting from the 36 trailing by 6 in the third. This week’s award goes to the $8 Million Dollar Man Mike Sherman who punted from the 41 twice in the second half, going on to blow their 42nd 6th lead of the season and losing the final chapter of the Texas-Texas A&M rivalry on a last second field goal.

Big Ten Projection Recap

On Aug 26th I posted projections for the season, it’s now time to pay the piper and see how they PAN‘d out.

Team: Pred W, Pred B1G W

Illinois: 8.0, 4.5

Indiana: 2.9, 0.6

Iowa: 7.8, 4.6

Michigan: 8.0, 4.8

Michigan St: 8.0, 4.7

Minnesota: 3.9, 1.2

Nebraska: 10.1, 6.1

Northwestern: 3.9, 1.7

Ohio: 9.3, 5.8

Penn St: 8.5, 5.2

Purdue: 5.7, 2.7

Wisconsin: 10.3, 6.3

That’s an average error of 1.4 games/team in total and 1.3 in conference play. Ohio was clearly my biggest miss, missing both numbers by about 3 games. Wisconsin was dead on and Iowa, Minnesota, Penn St and Purdue were all pretty close. I had the top and bottom of the Woody division correctly ranked but the middle was a mess. For the Bo division I swapped Nebraska and Sparty both nailed the other 4.

Nationally, picking conference winners went decently. Virginia Tech is favored in the ACC title game, along with other picks of mine like Wisconsin and Oregon. West Virginia is right in the middle of the Big East mess. If Alabama could make a field goal they would be playing for the SEC title and Oklahoma is playing for the Big XII’s BCS berth at bedlam.

In the smaller conferences, Tulsa, Toledo, Boise and Nevada all had shots but fell just short of championships while Troy wasn’t even close in the Sun Belt.

Advanced Metrics All-B1G

Offensive players are listed as PAN (per game)/WPA (total). OL is excluded because I have no stats specific to players. TE are evaluated solely on receiving. Defensive players are listed as Plays/Value (count and magnitude of plays made negative to the offense). Kickers and punters are cumulative for the season.

This is not meant to be absolute, but it is a ranking based solely on the advanced metrics, no judgment calls on my part.

Ryan van Bergen, Mike Martin and Kenny Demens all narrowly missed spots on the second team defense.

Upcoming Schedule

Don’t know if articles will be coming weekly, but I have a number of articles and ideas in the hopper for the pre and post-bowl season.

A bowl game preview

The promised Game Theory Manifesto

A 4th down redux, a more detailed look at fourth down decision making with an added tool of offensive and defensive strength sliders for dynamic decision making.

A critique of success rates and the concept of “staying ahead of the chains”

A semi-related post on why I think the running back position is overrated

A more detailed looks at the EV and WPA implications tied to UFR.

EV and WPA by coaches and if I can find a good source of history, coordinators, as well.

Some recruiting themed posts around signing day on the back of a massive recruiting database I am building on the back of my play by play database. I think there is a lot of potential here, just don’t know if I can pull it off.

Any user submitted ideas that are sure to be better than what I have listed so far.

I agree with the sentiment that the RB position is overrated. Especially these days with the rules so much in favor of passing attacks, the quarterback position is the clear king. The scheme and strategy/game plan is also important, which is why I believe that coaches are very underrated.

Coaches always talk about RBs "getting stronger as the game goes on" when you have one back getting the bulk of the carries. Is there anything at all to this? Is it just the defense tiring (so would any other back that comes in have equally improved stats)?

Is there anything to having a quarterback in a rhythm/taking him out of rhythm?

Are 4th down plays in critical game situations more or less likely to be successfully converted than equivalent down and distances in non-critical situations?

Mathlete, you've done a great job all year and I really, really appreciate the content you add to the blog. Your predictions throughout the course of the season has always calmed my nerves a bit on Saturday afternoons.

Do you think an adjustment needs to be made to your formula's to better weight the DEs/LBs? They seem to get HUGE numbers based on what's probably lots of tackles and/or sacks? I find it a little head scratching that a good to great DE is more valuable to winning a game over a good to great quarterback (see: Russell Wilson).

I probably didn't make this clear enough when I wrote up the article but the defensive numbers are season cumulative, where the offensive numbers are per game averages. Plus the defensive players are just noted for their good plays, I don't have any way to assign blame for the bad plays based on PBP data. A tackle after a 20 yard gain may be saving a TD or it could be covering a blown assignment.

As a systems student I am curious how some rather fuzzy information (case: Brian Kelley's red face) impacts your prediction, if at all. I am pretty sure it is a big deal but I have no idea how that can be included.

Who would you vote for Big 10 Offensive Player of the year....despite the numbers above, I would have to cast my vote for Montae Ball, with Denard second. I dont, but from watching all year, I think Russel Wilson is a little overrated and he wouldnt crack my top-2

Also, will KState cover that big number (-11, i think) against Iowa State....the pickings are slim tomorrow with so few games and I need a winner. So, give me at least one!!

I think Russel Wilson is the best player in the Big 10 this year, I'll just be rooting against him because, like, he only played one year in the Big 10. I know, I know, the award is based off of one year, but I'm just being irrational.

goes to Wisconsin OL. They open up big holes for Ball to run through. Watch them play, you'll see them pushing DL back with ease. Konz and Ziegler are one of the top interior OL prospect for NFL Draft. Konz is a potential 1st rounder while Ziegler is a potential 2nd rounder.

Two thoughts and I apologize if you had addressed them earlier in a previous post.

1. Plays not made are also important but are much harder to quantify. In the OSU game everyone saw that Posey was wide open at the end and had Braxton Miller not overthrown him he probably would have scored leaving the Wolverines with about 1:30 to win the game. Assuming they would start from the 20 they would have to get down at least under the OSU 30 to have a confident field goal attempt. Certainly not impossible and after the ND game UTL we know that miracles do happen. But that single play would have change the possible outcome from a high UM probability to a pretty good OSU probability. Those aren't factored in. Plays not made are always going to be tough. In the 1997-1998 Rose Bowl Charles Woodson was within a hair's breadth of sacking Ryan Leaf several times but it didn't happen. Had that happened the game wouldn't have been as close. Is it possible for you run a simulation of what the odds would be if Posey had scored? If you are familiar with computer chess programs look at Fritz (by Chessbase) it provides a similar analysis of who is "ahead" in the game and one can see graphically in much the same way you depict where the critical turning point occured. It has one huge advantage in that it can analyze the alternative lines of play.

2. Which leads to the second point and suggestion. Others have also noted that time and situation affect the effect of any individual play. Any single play doesn't exist in isolation. Failing to gain yardage on 2nd down leading to 3rd and long is a common example. What I'm curious is about is whether your analysis can shed light on the relative value of points and leads during the game. In the Nebraska and OSU games the visitors scored first but they did so very early on the game. So one could surmise that a +7 score for a team with 50 some minutes of time isn't crucial. Being +1 with 0:30 might be conclusive unless the ball is within easy field goal range for your opponent. The thought occurred that it might be another way of looking at how well a defense plays. A great defensive team could hold a small +lead over a longer period of time than a poor one. In traditional football terms, the great defense needs only to be ahead to win most of the time and not two scores or three scores. Is that calculable? I suspect this season our defense would do well. We took the lead in the 3rd quarter and while OSU did score another 10 points, we never trailed.

*Add a play here for “Fitz TD”. Then “undo” it to the actual result of the replay overturn call.

Part 2:

2nd and 10 at OSU 20 Braxton Miller rush for 4 yards to the OhSt 24.

3rd and 6 at OSU 24 Timeout OHIO STATE, clock 1:48.

3rd and 6 at OSU 24 Braxton Miller pass incomplete to DeVier Posey.*

4th and 6 at OSU 24 Braxton Miller rush for 7 yards to the OhSt 31 for a 1ST down.

*Add a play here (and this is fudging a little bit more) for OMG Posey is behind the defense and a 60yd bomb is in the air and he should score on this play. Then “undo” it to the actual result of incomplete, wow we’re lucky that Miller missed that throw.

Hey Mathlete, in the 11/23 Wall Street Journal, Accuscore said Michigan had a 60% chance of winning, based on 10,000 game simulations. Your initial probability had Michigan around 71-72%. What methodologies do you and they use to come up with these initial probabilities? Have you tracked yours versus theirs to see who is more accurate?